JP6605506B2 - Methods, kits, and systems for scoring immune responses against cancer by simultaneous detection of CD3, CD8, CD20 and FoxP3 - Google Patents

Description

  This disclosure relates to methods, kits, and systems for scoring an immune response against cancer by examining tissue infiltrating lymphocytes (TIL).

  Biological samples such as histology specimens can be examined histopathologically by light microscopy using bright field illumination; while molecular pathology is the molecular level of biomolecules associated with a disease. This is an inspection. Histopathological examination can reveal important information regarding patient diagnosis, prognosis, and treatment options. A pathologist studies the histopathological architecture, tissue morphology, and / or signals associated with the detection of specific biomolecules (eg, nucleic acids or proteins). Many currently available assays include proteins (ie, immunohistochemistry (IHC)), nucleic acids (ie, in situ hybridization (ISH)), sugars (ie, histochemistry (HC)), and enzymes (ie, enzymes). Histochemistry (EHC)) is detected and / or quantified.

  Although the existence of variable amounts of immune infiltrates in human solid tumors from various sources and from various patients has been demonstrated previously, the prognostic importance of these components is not yet fully understood. There has been a transition since the turn of the 21st century, and immunity and inflammation are considered to be prominent features of cancer. In other words, considering published data, it is known that local inflammation has a strong influence on the occurrence of cancer. In one example, acute local inflammation formation has been used for adjuvant therapy of superficial bladder cancer with BCG. This approach is now the most advanced technology for this malignancy. In contrast, it is understood that chronic inflammation affects the outcome and progression of many tumors (head and neck, stomach, colorectal, etc.). Similarly, immunodeficiency is seen as a negative prognostic indicator.

  However, in view of the prior art, an image analysis system for automatically calculating an immune score from a set image of multiple IHC slides or an image of fluorescent staining slides is desirable.

  Disclosed are methods, kits, and systems for scoring cancer by examination of tissue infiltrating lymphocytes (TIL), where a panel of CD3, CD8, CD20, and FoxP3 is a high-risk melanoma, colorectal It has been found useful as a prognostic marker for many cancers, including cancer and breast cancer.

  Immune cells (TIL) that infiltrate the tumor microenvironment can limit or promote tumor progression. There is increasing evidence that the number, type and location of TILs in the primary tumor can have a prognostic role. These findings were derived from the immune context in the tissue and led to the development of a potential new scoring system based on the identification and evaluation of specific lymphocyte populations. The potential prognostic importance of CD3, CD8, CD20, and FOXP3 was explored as an “Immunoscore” for melanoma patients who would utilize widely available standardized techniques.

  The role of adaptive immune responses in the control of human tumor growth and recurrence has been extensively studied. Characterization of tumor-infiltrating immune cells in cancer has been shown by gene expression profiling and in situ immunohistochemical staining. Collectively, however, immunological data (type, density, and location of immune cells in a tumor sample) is, for example, more than histopathological methods currently used to stage colorectal cancer. Was also found to be a good predictor of patient survival. The results were verified in two additional patient populations. These data support the hypothesis that the adaptive immune response affects the behavior of human tumors. Thus, in situ analysis of tumor-infiltrating immune cells can be a valuable prognostic tool in the treatment of colorectal cancer and possibly other malignancies.

  The natural course of the tumor includes stages of “in situ” growth, invasion, extravasation and metastasis. During these phases, tumor cells interact with their microenvironment and are affected by signals coming from stroma, endothelial cells, inflammation and immune cells. In fact, tumors are often infiltrated by various numbers of lymphocytes, macrophages or mast cells. While the latter produces factors that maintain chronic inflammation and promote tumor growth, it is generally believed that lymphocytes can control cancer outcomes, as has been demonstrated in mouse models. In this study, we analyzed data from a large cohort of human tumors, with primary tumor invasion by Th1 and cytotoxic types of memory T cells being the strongest prognostic factors in terms of release from disease. Establish clearly that there is overall survival at all stages of clinical disease. We review data suggesting that the tertiary lymphoid structures adjacent to the tumor and consisting of mature dendritic cells (T and B cells organized as germinal centers) may be the site of anti-tumor response. To do. In addition to staging the lymph node metastasis of tumors, we predict the disease-free survival and support the decision on adjuvant therapy in early human cancers as a new prognostic factor, type and density of lymphocyte infiltrate And we propose immune scoring based on location.

  An unmet medical need is the ability to determine which melanoma patients with nodal involvement will progress to metastasis, not based on measurements of the patient's immune response. The current standard of practice is to either monitor and monitor these patients or treat them with harsh interferon therapy.

Multiple IHC slides have the potential advantage of simultaneously identifying multiple biomarkers in tissue sections, unlike single biomarker labels on multiple slides. To assist pathologists in the slide interpretation process of multiple IHC slides, digital pathology visually separates and quantitatively scores biomarkers for immune cells, such as different types of T cells and B cells. Can help. The following document discloses a method for assessing and / or scoring tissue but is not sufficient for multiple slides: Manual tumor microenvironment assessment with CD3, CD8, CD45R0 and FoxP3: Paolo Ascierto, " The Medical Need for an Immune Scoring / Profiling for Melanoma and Other Cancer Patients, "Invited talk, Tucson Symposium, Mar 12-13, Tucson, AZ, 2013; Manual / semi-automatic CD3 / CD8 counting: Galon J Costes A Sanchez- Cabo F Kirilovsky A Mlecnik B Lagorce-Pages C Tosolini M Camus M Berger A Wind P Zinzindohoue F Bruneval P Cugnenc PH Trajanoski Z Fridman WH Pages F "Type, density, and location of immune cells within human colorectal tumors predict clinical outcome," Science 29; 313 (5795): 1960-4, 2006; Flow cytometry counting of CD8 and FoxP3: F. Stephen Hodi, "Recent Advances in Combinatorial Therapies," Invited talk, Tucson Symposium, Mar 12-13, Tucson, AZ, 2013 ; Immune scoring from H & E: Sherene Loi, Nicolas Sirtaine, Fanny Piette, Roberto Sal gado, Giuseppe Viale, Franc, oise Van Eenoo, Ghizlane Rouas, Prudence Francis, John PA Crown, Erika Hitre, Evandro de Azambuja, Emmanuel Quinaux, Angelo Di Leo, Stefan Michiels, Martine J. Piccart, and Christos Sotiriou, "Prognostic and Predictive Value of Tumor-Infiltrating Lymphocytes in a Phase III Randomized Adjuvant Breast Cancer Trial in Node-Positive Breast Cancer Comparing = the Addition of Docetaxel to Doxorubicin With Doxorubicin-Based Chemotherapy: BIG 02-98, "Journal of Clinical Oncology 31 (7) , pp 860-867, 2013.
The technical hurdle was to quantify the immune cell response on one slide and incorporate the necessary immune markers. In order to fully understand the status of a patient's current immune response to cancer, tumor markers, B cells, total T cells, cytotoxic T cells, and regulatory T cell populations should all be seen together. Yes, scoring and interpretation must be automated and measurable to function realistically as a clinical concept.

  There are ways to multiplex several markers, but to our knowledge, these five markers are incorporated into the assay to address this particular medical need and assays involving digital algorithms can be either research or commercial. Is not presented.

  We have developed a multiplex assay of research prototypes and corresponding digital scoring algorithms based on clinical, biological, and statistical inputs. Our assay includes one tumor marker as well as four immune markers.

  In summary, immunological data (type, density, and location of immune cells within a tumor sample) are predictors of better patient survival than the histopathological methods currently used for cancer staging. It was found that For colorectal cancer, the results were validated in two patient populations. This data supports the hypothesis that the adaptive immune response affects the behavior of human tumors. Thus, in situ analysis of tumor-infiltrating immune cells can be a valuable prognostic tool in the treatment of colorectal cancer and other malignancies.

  Image analysis algorithms and / or systems have been developed that automatically calculate immune scores from set images of multiple IHC slides and / or fluorescently stained slides. The image analysis algorithm is a computer-implemented method for counting the number of cell types in a single tissue specimen that is stained in a multiplex assay, comprising lymphocyte markers CD3, CD8, CD20, FoxP3 and tumor detection Imaging the single tissue specimen stained with the multiplex assay comprising a marker; images of the single tissue specimen stained with the multiplex assay in separate image channels for each marker of the multiplex assay A step of identifying a region of interest in each image channel based on intensity information in each channel, wherein low intensity regions in each channel are removed, and high intensity regions are cell signals. A single surrogate image, wherein the surrogate image is an image channel for all lymphocyte markers. A step of applying a cell detection algorithm, wherein the cell detection algorithm is a membrane discovery algorithm or a nuclear discovery algorithm; an image of each image channel or binding channel, or a grayscale or absorbance image, etc. Identifying features of lymphocytes and lymphocyte combinations in transformed images or surrogate images; training a classification algorithm based on characteristics of known lymphocytes and lymphocyte combinations; detecting cells as false positive cells, CD3 Images of each image channel or binding channel, or a grayscale or absorbance image, etc., specified to classify the detection cells as only T cells, CD3 and CD8 T cells, at least one of FP3 T cells, and CD20B cells Conversion image or support Applying a trained algorithm to the characteristics of lymphocytes and lymphocyte combinations in a rogate image; counting the number of different types of each of the classified cells; scoring the tissue specimen. And a step based on the number of each cell type for which the score has been counted.

1 (A)-(B) are photomicrographs of primary melanoma tissue (A) and (B) taken with (A) 20 × objective lens and (B) 40 × objective lens, in dark brown color. Detected FoxP3 (exemplarily indicated by the arrow labeled “DB” in FIG. 1 (B)), CD8 detected in dark gray / black (labeled “DGB” in FIG. 1 (B)) CD3 detected in blue (exemplarily shown with an arrow labeled “B” in FIG. 1B), CD20 detected in red / purple (shown in FIG. 1B). (B) exemplarily indicated by an arrow labeled “RM”) and S100 detected in green (illustrated by an arrow labeled “G” in FIG. 1A) Shows five-fold staining, (B) is phosphorous visible on the left side of the primary tumor It has focused on the sphere cells.

2 (A)-(B) are photomicrographs of colon cancer tissues (A) and (B) taken with (A) 20 × objective lens and (B) 40 × objective lens, in dark brown color. Detected FoxP3 (exemplarily indicated by the arrow labeled “DB” in FIG. 2B), CD8 detected in dark gray / black (labeled “DGB” in FIG. 2B) CD3 detected illustratively in blue), CD3 detected in blue (exemplarily indicated by the arrow labeled “B” in FIG. 2B), CD20 detected in red / red purple (FIG. 2) (B) illustratively indicated by the arrow labeled “RM”) and cytokeratin (OSCAR) detected in green (exemplarily indicated by the arrow labeled “G” in FIG. 2B) 5) is shown.

FIG. 3 is a photomicrograph of tongue cancer tissue, FoxP3 detected in dark brown (exemplarily indicated by the arrow labeled “DB”), CD8 detected in dark gray / black (“DGB” CD3 detected in blue (exemplarily indicated by an arrow labeled “B”), CD20 detected in red / red purple (“RM”) Shown is a quintuple staining with cytokeratin (OSCAR) detected in green (exemplarily shown with an arrow labeled “G”) and shown with green arrows.

FIG. 4 is a photomicrograph of lung adenocarcinoma tissue taken with a 20 × objective lens, FoxP3 detected in dark brown (exemplarily indicated by the arrow labeled “DB”), dark gray / black CD8 detected by (indicated by the arrow labeled “DGB”), CD3 detected by blue (exemplified by the arrow labeled “B”), red / red purple 5 with detected CD20 (exemplarily indicated by arrow labeled “RM”) and cytokeratin (OSCAR) detected in green (exemplarily indicated by arrow labeled “G”) Double staining is shown.

FIG. 5 shows cytokeratin tumor markers (yellow) (exemplarily indicated by the arrow labeled “Y”) and infiltrating CD8 + cytotoxic T cells (green) (exemplified by the arrow labeled “G”). Is a micrograph (20 × objective lens) of an ovarian adenocarcinoma tissue (5 fold) stained with an antibody against (shown in FIG. 2) further illustrating how CD8 cells infiltrated the entire tumor.

FIG. 6 shows proliferation markers Ki-67 (green) (exemplarily indicated by the arrow labeled “G”), CD8 + T cells (purple) (exemplarily indicated by the arrow labeled “P”). , And photomicrographs of normal tonsil tissue stained with antibodies to CD3 + T cells (yellow) (exemplarily shown by the arrow labeled “Y”). This image confirms the localization of Ki-67 + cells in germinal centers with CD8 and CD3 cells, FoxP3 was detected in dark brown and CD8 was detected in the surrounding mantle. Magnification x200.

FIG. 7 is a photomicrograph of a breast cancer tissue (20 × objective), purple tumor (exemplarily indicated by an arrow labeled “P”), green infiltrated CD8 + cells (labeled “G”). CD68 + macrophages in blue (exemplarily shown with an arrow labeled “B”) and CD20 detected in red (exemplified with an arrow labeled “R”) Is shown).

8 (A)-(D) are micrographs similar to (A) FIG. 7, where (B) and (D) are unmixed into their component colors, and (C) are recombined and pseudo-colored. Has been.

FIG. 9 is a schematic diagram illustrating the workflow of the scoring algorithm according to the present disclosure.

FIG. 10 is a schematic diagram illustrating an example of execution of a scoring algorithm according to the present invention.

11 (A)-(B) are schematic diagrams showing a filter bank (A) and a ring detector (B) according to the present invention.

  There is an unmet medical need to develop the ability to determine which melanoma patients with nodal invasion progress to metastasis, not based on measurements of the patient's immune response. The current standard of practice is to either monitor and monitor these patients or treat them with harsh interferon therapy.

  The technical hurdle was to quantify the immune cell response on one slide and incorporate the necessary immune markers. In order to fully understand the status of a patient's current immune response to cancer, tumor markers, B cells, total T cells, cytotoxic T cells, and regulatory T cell populations should all be seen together. Yes, scoring and interpretation must be automated and measurable to function realistically as a clinical concept.

  There are ways to multiplex several markers, but to our knowledge, these five markers are incorporated into the assay to address this particular medical need and assays involving digital algorithms can be in either research or commercial space. Is not presented. Our assay includes one tumor marker as well as four immune markers.

  Multiplex assays, along with image analysis algorithms, potentially provide a toolkit for calculating breast and / or melanoma immune scores. The final report generated by the computer supports clinical analysis of the patient's personal immune response and can be used to assist in further treatment, but further research is needed to demonstrate this hypothesis .

TIL
Immune cells (TIL) that infiltrate the tumor microenvironment can limit or promote tumor progression. There is increasing evidence that the number, type and location of TILs in the primary tumor can have a prognostic role. These findings were derived from the immune context in the tissue and led to the development of a potential new scoring system based on the identification and evaluation of specific lymphocyte populations. We explored the potential prognostic importance of CD3, CD8, CD20, and FOXP3 as an “Immunoscore” for melanoma patients who would utilize widely available standardized techniques.

  We have developed a multiplex assay of research prototypes and corresponding digital scoring algorithms based on clinical, biological, and statistical inputs.

  Multiple IHC slides have the potential advantage of simultaneously identifying multiple biomarkers in a tissue section, unlike single biomarker labels on multiple slides, so that within a single piece of tissue To demonstrate more information. With this objective in mind, a multiplex assay has been developed to provide an immune score for patients suffering from breast cancer and melanoma along with other tumor types. To assist pathologists in the slide interpretation process, digital pathology can help to visually separate and quantitatively score biomarkers for immune cells, such as different types of TIL.

CD3
CD3 is generally a “universal marker” for T cells. Further analysis (staining) must be done to identify T cell types such as regulatory or cytotoxic T cells. Analysis of CD3 + T cell count is a good indicator of immune response.

CD8
CD8 is a specific marker for cytotoxic T lymphocytes. These are effector cells that actually “kill” tumor cells. They act by direct contact, introducing the digestive enzyme granzyme B into the cytoplasm of the tumor cells, thereby killing it. Therefore, it is important to know not only the total number but also its position relative to the tumor itself. Only those cells that are in direct contact with the tumor cells will be effective in killing the cells.

CD20
CD20 is a marker for antibody-producing B cells. Thus, it is an indicator of a more fully expressed (mature) immune response. The total number and location of these antibody producing effector cells can be important.

FoxP3
FoxP3 is a nuclear transcription factor that is the most specific marker of regulatory T cells (so-called Tregs). Tregs function to moderate the immune response. Presumably the presence of Treg will indicate an inhibitory immune response against the tumor.

Tumor markers In epithelial tumors (carcinomas), cytokeratin staining distinguishes tumor cells as well as normal epithelium. This information, together with the fact that tumor cells abnormally overexpress cytokeratin compared to normal epithelial cells, makes it possible to distinguish between tumor and normal tissue. In melanoma tissue derived from neuroectodermal, the S100 biomarker serves a similar purpose.

Primary Staining / Bluing / Counter Staining In an exemplary embodiment, the method includes applying a chromogenic reagent such that the sample is specifically stained. In one embodiment, specific staining involves the application of a primary stain that selectively stains a portion of the sample via adhesion associated with hydrophobicity, intercalation, or other non-recognition associations. For example, hematoxylin and eosin staining (H & E staining) are well known in the art. Reference is made to US Patent Application Publication No. 2008/0227143, which is hereby incorporated by reference for disclosure relating to hematoxylin and primary staining. H & E staining is used to assess cell morphology and is a major tool for pathological diagnosis of cancer.

Detection The method includes applying an immunohistochemistry (IHC) binding reagent or an in situ hybridization (ISH) binding reagent such that the IHC binding reagent or ISH binding reagent contacts the sample. ISH can be used to diagnose the presence of genetic abnormalities or diseases, or over or under expression from standards. For example, ISH can be used to detect gene amplification, deletion, or translocation associated with a particular disease. ISH is also useful for the diagnosis of infectious diseases because it allows detection of microbial and viral sequences in infected cells. IHC includes antibodies that specifically bind to the epitope of interest. Epitopes, also called antigens or antigen sequences, are portions of proteins that have been established as markers of clinical interest. For example, an epitope can be a normal protein expressed at either a higher or lower concentration than normal, such as in a mutant form of a protein, a protein-protein binding site, or a control sample. Detection and / or quantification of epitopes in various biological samples has been used for a vast number of clinical purposes.

  Both IHC and ISH involve specific recognition events between the nucleic acid probe (ISH) or antibody (IHC) and the target in the sample. This specific interaction labels the target. The label can be visualized directly (direct label) or can be observed indirectly using additional detection chemistry. Color detection with deposition of chromogenic material in the vicinity of the label includes an additional detection step that amplifies the intensity of the signal to facilitate visualization. Visualization of the amplified signal (eg, use of a reporter molecule) allows the observer to localize the target in the sample.

Color detection provides a simple and cost-effective detection method. Chromogenic substrates have traditionally functioned by precipitation when acted upon by appropriate enzymes. That is, traditional chromogenic materials are converted from soluble reagents to insoluble colored precipitates upon contact with the enzyme. The resulting colored precipitate does not require any special equipment for processing or visualization. Table 1 is a non-exhaustive list of chromogen systems useful within the scope of this disclosure.

  Table 1 is not exhaustive but provides insight into the various types of chromogenic substances currently available († WO 2012/024185, Kelly et al. “Substrates for Chromogenic detection and methods of use in detection. assays and kits ”).

  An important advance to the present invention is the use of tyramide-chromogen conjugates that allow the deposition and covalent attachment of novel chromogens in new color schemes. This covalent bond also has the added advantage that the specimen slide can be dehydrated with a series of increasing concentrations of alcohol solution followed by xylene and can be mounted to a hardened plastic for permanent storage. This results in improved staining morphology and resolution compared to other chromogenic dye staining techniques using dyes extracted by this general histological process.

  One aspect of the present disclosure is that chromogen conjugates can be configured to absorb light more selectively than traditionally available chromogens. Detection is achieved by absorption of light by the signaling conjugate; for example, an absorbance of at least about 5% of the incident light will facilitate detection of the target. For other darker stains, at least about 20% of the incident light will be absorbed. Non-uniform absorbance of light in the visible spectrum results in a chromophore that appears colored. The signaling conjugates disclosed herein may appear colored due to their absorbance; signaling conjugates may appear to provide any color when used in an assay, certain The colors include red, orange, yellow, green, indigo, or purple, depending on the spectral absorbance associated with the chromophore moiety. According to another aspect, the chromophore moiety may have a spectral absorbance that is narrower than the absorbance of traditionally used chromogens (eg, DAB, Fast Red, Fast Blue). In exemplary embodiments, the spectral absorbance associated with the first chromophore portion of the first signaling conjugate is between 30 nm and 250 nm, between 30 nm and 150 nm, between 30 nm and 100 nm, or between 20 nm and 60 nm. With a full width at half maximum (FWHM) in between.

  The narrow spectral absorbance allows the signaling conjugate chromophore moiety to be analyzed differently than traditional chromogens. While having improved features compared to traditional chromogens, the detection of signaling conjugates remains simple. In an exemplary embodiment, the detection includes using a bright field microscope or equivalent digital scanner. The narrow spectral absorbance allows chromogenic multiplexing at levels that exceed the capabilities of traditional chromogens. For example, traditional chromogens are somewhat duplexed on a daily basis (eg, fast red and fast blue, fast red and black (silver), fast red and DAB). However, triple or trichromatic applications, or more, are unusual because it becomes difficult to distinguish one chromogen from another, especially when they overlap or coexist. In an exemplary embodiment of the technology disclosed herein, the method includes detecting at least two to six or more using different signaling conjugates or combinations thereof. In one embodiment, illuminating the biological sample with light comprises illuminating the biological sample with a light source having a narrow spectral width, the light source having a narrow spectral width being between 30 nm and 250 nm, 30 nm, It has a spectral emission with a second full width at half maximum (FWHM) of between 150 nm, 30 nm and 100 nm, or between 20 nm and 60 nm. In other embodiments, illuminating the biological sample with light includes illuminating the biological sample with an LED light source. In other embodiments, illuminating the biological sample with light includes illuminating the biological sample with a filtered light source.

  Reference is made to US Patent Application Publication No. 20130260379, which is hereby incorporated by reference in its entirety. Reference is also made to U.S. Patent Application No. 61/831552, which is hereby incorporated by reference in its entirety.

Automatic scoring; algorithm In an exemplary embodiment, the subject disclosure executes on a computer executed by a processor to perform operations including training the algorithm, calculating a score, and unmixing A non-transitory computer readable medium for storing possible instructions. In another exemplary embodiment, the present subject disclosure is a digital pathology system that includes a processor and a memory coupled to the processor that, when executed by the processor, performs algorithm training, score calculation, and the like. And computer-executable instructions that cause the processor to perform operations including unmixing.

  The present invention includes an image analysis system that automatically calculates a score, eg, an immune score, for a multiplex assay (eg, a set of multiplex IHC slides) or fluorescently stained slides or groups of slides. The image analysis system includes software modules and algorithms that include computer-executable instructions, eg, executed by a processor, to analyze images of tissue stained with a multiplex assay and to generate a score based on the analysis including. The image analysis system can identify multiple biomarkers in the same biological specimen, eg, a single tissue specimen, and is applicable to multiplex assays for any tissue type.

  For illustrative purposes, the image analysis algorithm of the present invention will be described in the context of a cancer tissue slide that has been stained by a multiplex assay. As an example of an implementation of the image analysis algorithm of the present invention, a breast tissue slide is analyzed. In an exemplary embodiment of the invention, the stained tissue slide is imaged and / or scanned and a corresponding color image is created and input into the image analysis system. Alternatively, the image is obtained from a certain source.

  The present invention is applicable to bright field and fluorescent images, where a microscope system (eg, bright field microscope system) or scanner is utilized to image or scan a slide to obtain a bright field image. To create a multispectral image, a multispectral imaging system (eg, a fluorescence microscope or a scanner (eg, a full slide scanner) is used to obtain a multispectral fluorescent image.

  In order to analyze the created or obtained image, the image analysis algorithm and / or system of the present invention provides an unmixing module that identifies the individual constituent dyes for biomarkers and the proportion of the dyes appearing in the color image. Includes color unmixing algorithms that can be included. The unmixing work is described in US Provisional Application No. 61/830620, filed June 3, 2013 in the name of the invention, IMAGE ADAPIVE PHYSIOLOGICALLY PLAUSABLE COLOR SEPARATION, the entire disclosure of which is incorporated herein by reference. It can be performed according to the disclosed unmixing method. Based on the image channels separated for each marker, detection algorithms have been developed to automatically identify different combinations of T and B lymphocytes for each distinct IHC marker. For example, different T cell markers may coexist and produce different combinations of dye amounts. In addition, tumor markers, such as cytokeratin markers (eg Oscar), to identify the tumor, intratumoral and / or surrounding area (eg to identify the nucleus and tumor area) to obtain tissue context Can also be included in the assay. In an exemplary embodiment, image analysis is performed after unmixing to identify, quantify, and / or generate a score for biomarkers, such as Oscar, CD3, CD8, and FoxP3 markers. In an exemplary embodiment of the invention, the score for each marker can be reported as output along with the organizational context information.

  FIG. 9 shows the workflow of the image analysis method and system of the present invention. FIG. 9 shows an execution example of the image analysis method according to the present invention, and shows details regarding the image analysis method applied to the calculation of the breast cancer immunity score.

  The image analysis method of the present invention includes a training stage that can be incorporated into a training stage module. In the training phase, identification of different types of lymphocytes as well as examples of false positive cells can be performed first for each marker. For example, in FIG. 9, after automatic detection, intracellular seeds (eg, true positive T cells) and extracellular seeds (eg, false positive T cells) are manually annotated.

  Next, one or more feature extraction algorithms according to the present invention, which can be incorporated into the feature extraction module, are applied to the image patch around the seed and features for each type of cell (eg, CD3 cells, CD8 cells and CD20 cells). A set is obtained. The features are extracted from different image channels, such as the RGB channel of the image, the absorbance space, the unmixing channel and the grayscale image. Here, different feature extraction algorithms can be used. For example, as shown in FIG. 9, dictionary learning can be applied to build codebooks for different cell appearances.

  In an exemplary embodiment of the invention, a classification algorithm that can be included in the classification module can be trained to classify different types of cells present in an image, eg, an image of a tissue stained with a multiplex assay. In an exemplary embodiment of the invention, different classification algorithms may be applied, such as SVM, random forest and boosting algorithms. In an exemplary embodiment of the invention, the SVM algorithm is utilized in a classification algorithm to associate a feature with a label for that feature as shown in FIG.

  In the test stage and / or the execution stage to which the classification algorithm according to the present invention is applied, the entire slide image or a part thereof is input into the image analysis method / system of the present invention. Unmixing, removal of tumor-related noise, creation of surrogate images, ring or nodule detection (eg to find lymphocytes with membrane markers), features for each target area in the whole slide image or part of it Extraction, classification, and cell or lymphocyte type counting steps are applied. A surrogate image of lymphocytes (ie, an image containing all T cells) can be obtained first. By doing so, we can first detect all T cells and then use a classification algorithm to identify the cell type. To create a surrogate image, a log transform is used to convert the RGB image to absorbance space, and then we have not detected tumor cells, so the Oscar channel is noisy (as shown in FIG. 9). Can be used to remove tumor cells). In an exemplary embodiment of the invention, Oscar channel information including tumor cell information is stored in memory for counting tumor cells. Since unmixing of multiplex assays, eg, assays with 5 or more markers, may be unreliable due to large color variations across the image, surrogate images are utilized to perform detection instead of each unmixing channel. In an exemplary embodiment of the invention, some other type of composite image may be utilized, such as a transformed image (eg, an absorbance image or a grayscale image) or an image that combines various channels. The input image (eg, RGB image) can then be unmixed into different channels of the biomarker, such as Oscar, FoxP3, CD3, CD8, HTX, and CD20 channels.

  A cell detection algorithm (eg, corresponding to the ring detector of FIG. 10) may be included in the cell detection module and applied to the surrogate image to detect lymphocytes. In an exemplary embodiment of the invention, the ring shape of the cell detection algorithm results in the capture of a ring shape membrane marker (eg, may include detection of all possible T cell candidates and false positives). We use the absorbance image with Oscar correction as the surrogate image in the implementation (FIG. 9) to detect the ring-shaped membrane marker. In the execution phase, a very sensitive cell detection algorithm is desirable, for example an algorithm that can capture all possible cells and include false positive detection. In an exemplary embodiment of the invention, a ring detector can be applied to a surrogate image to detect membrane markers that are utilized to identify cells. FIG. 10 shows a simple example of a ring detector. It should be understood by those skilled in the art that other different types of cell detection algorithms can be applied to images (surrogate or otherwise) to detect cells.

  A classifier, eg, a classifier learned or created from the training stage, then all cell candidate seeds between categories, eg false positive cells, CD3 only T cells, CD3 / CD8 T cells, FP3 T cells and B Can be used to sort cells.

  In an exemplary embodiment, information from the CD3, CD8, FP3, and CD20 channels, such as information about the color and shape of cells in each channel, can also be used as features for classification. FIG. 9 shows an execution example of the dictionary learning algorithm. In this example, (1) the learned appearance features, such as the shape of the cells obtained from the local patches around the seed, are used to distinguish true positive lymphocytes from false positive lymphocytes (2 ) The characteristic of the amount of staining is used, for example, to classify CD3 only T cells and CD3 / CD8 T cells.

  In particular, for each true positive T cell detected in the surrogate image, CD3 and CD8 pixels around the T cell neighborhood from the CD3 and CD8 unmixing channels can be obtained, and the ratio of the two stains is Calculated to classify between (1) CD positive and CD8 positive cells and (2) CD3 positive only cells. In an exemplary embodiment of the invention, if (CD8 pixels) / (CD3 pixels) <threshold, cells identified in the surrogate image are labeled as CD3 only cells, otherwise CD3 / CD8 Labeled as a cell.

  To identify the nuclei, we evaluate the FoxP3 channel image because the nuclear marker channel, eg FoxP3, is a nuclear marker. We apply a nodule detection algorithm that can be included in the nodule detection module within the FoxP3 channel to find the FoxP3 nucleus. In another embodiment of the present invention, a radial symmetry algorithm that can be included in the radial symmetry module can be utilized to detect nuclei. However, it should be understood by those skilled in the art that other blob detection methods may be achieved using, for example, circle transformation.

  The Oscar channel is used to identify the tumor area. In an exemplary embodiment of the invention, a region detection algorithm can be used to identify a tumor region. In the implementation shown in (FIG. 11), the Otsu threshold method is applied to the Oscar channel to find the tumor area. The Otsu threshold method identifies tumor areas by a method that finds the best threshold for foreground and background. The Oscar channel is also used to remove noise associated with the tumor area. The Oscar channel is also utilized to remove the ring-shaped tumor cytoplasmic marker. Since cytoplasmic markers sometimes create a ring-like appearance, we need to first remove that region before applying ring detection. We note that the order of the steps of the image analysis algorithm, method and / or system of the present invention can vary.

  FIG. 11B shows an exemplary ring detector according to the present invention that can be used for lymphocyte detection in our implementation. To implement a ring detector, a filter bank, ie a set of masks containing 12 directions, can be constructed, as shown in FIG. The black area shows -1 pixels covering the ring center and the gray area corresponds to 1 pixel covering the ring boundary, so that when this mask is convolved with the image, we To get the difference in intensity. In this example, the width of the filter is 3 pixels, the length of the black area is 5 pixels, and the length of the gray area is 10 pixels. For example, parameters including the number, width, and length of filter directions can be adjusted, for example, to correspond to different cell sizes that are desired to be detected. As shown in FIG. 11B, for each position (x, y) in the image, voting V (x, y) = V (x, y) + [sum (I (A)) − sum (I (B))] is calculated. The voting corresponds to the difference in intensity values of all pixel intensities in the filter black area and the filter gray area. This vote is accumulated at (x, y) along 12 directions in the center of the ring. Note that 12 is just an example number of directions, and a different number can be used. In an exemplary embodiment of the invention, multi-scale detection (ie, different sized rings) can be utilized to capture different sized cells by adjusting filter parameters.

  One embodiment relates to a user interface and functionality associated with an image analysis system and / or method according to the present invention. The user interface provides the following functions:

  An image loading function that allows loading of an FOV or full slide image via a “Load Data Dir” button. For example, a tissue region detection function that enables detection of lymphocytes and tumor regions and / or display of detection results by clicking a file name in the “File List” panel.

  A cell modification function that allows the user to modify cell detection results, add annotations and / or delete them. In an exemplary embodiment of the invention, the user can first select a cell type and change it from the pop-up list, then click the “Modify Cell” button to begin editing, and left click on the mouse Corresponds to, for example, that a seed can be deleted, and a right mouse click corresponds to, for example, adding a seed. After editing is performed, activation of the “Enter” key on the keyboard indicates that editing is complete.

  The tumor correction function allows the user to change the tumor detection result. In an exemplary embodiment of the invention, by clicking on the “Add Region” and “Remove Region” icons or areas of the user interface, the user can begin to draw a polygon, for example by left-clicking the mouse, Allows the user to indicate that polygon drawing is complete.

  The save changes feature allows the user to save changes via activation of the “Save Modification” button.

  The final report creation function allows the user to create a final report based on the number of one or more cell types, for example including an immune score. For example, the user can click on a “Get Report” button or an area of the user interface to create a final report. It should be understood by those skilled in the art that buttons, icons, and screen areas can be used to enable functionality and all can be used as alternatives to others.

  Exemplary embodiments of computer systems used in accordance with the present disclosure may include any number of computer platforms or types of computer platforms such as workstations, personal computers, servers, handheld devices, multiprocessor systems, microprocessor-based or It may include a programmable consumer electronics, network PC, minicomputer, mainframe computer, or any other current or future computer. An exemplary implementation may also be in a distributed computing environment where tasks are performed by local and / or remote processing devices that are connected to a communication network (eg, by hard-wired connections, wireless connections, or combinations thereof). Can also be implemented. In a distributed computing environment, program modules can be located in both local and remote computer storage media including storage storage devices. However, it will be appreciated by those skilled in the art that the foregoing computer platform described herein is specifically configured to perform the special operations of the described invention and is not considered a general purpose computer. .

  A computer typically includes known components such as a processor, an operating system, system memory, a storage and storage device, an input / output controller, an input / output device, and a display device. It will also be appreciated by those skilled in the relevant art that there are many possible configurations and components of a computer and may also include a cache memory, a data backup unit, and many other devices. Examples of input devices include a keyboard, a cursor control device (eg, a mouse), a microphone, a scanner, and the like. Examples of output devices include display devices (eg, monitors or projectors), speakers, printers, network cards, and the like. The display device may include a display device that provides visual information, which typically may be logically and / or physically organized as a pixel array. An interface controller can also be included that can include any of a variety of known or future software programs for providing input and output interfaces. For example, an interface may include what is commonly referred to as a “graphical user interface” (often referred to as a GUI) that provides one or more graphical representations to the user. The interface is typically adapted to accept user input using means of selection or input known to those skilled in the relevant art. The interface can also be a touch screen device. In the same or another embodiment, applications on the computer may use an interface that includes what is referred to as a “command line interface” (often referred to as CLI). CLI typically provides text-based interaction between applications and users. Typically, a command line interface presents output and receives input as a text line through a display device. For example, some implementations include what are referred to as “shells”, such as Unix Shell known to those skilled in the relevant art, or Microsoft. It may include Microsoft Windows Powershell using an object-oriented programming architecture such as the NET framework.

  One skilled in the relevant art will understand that an interface may include one or more GUIs, CLIs, or combinations thereof.

  The processor may include or be available or available from a commercially available processor, such as a Celeron, Core, or Pentium processor manufactured by Intel, a SPARC processor manufactured by Sun Microsystems, an Athlon, Sempron, or Opteron manufactured by AMD. It can be one of the other processors that become capable. Some implementations of the processor may include what are referred to as multi-core processors and / or may be able to use parallel processing techniques in a single or multi-core configuration. For example, a multi-core architecture may typically include two or more processors “execution cores”. In this example, each execution core can be executed as an independent processor that allows parallel execution of multiple threads. Those skilled in the art will also appreciate that the processor may be configured with what is commonly referred to as a 32 or 64 bit architecture, or other architectural configurations now known or that may be developed in the future.

  The processor is typically, for example, a Windows type operating system from Microsoft; a Mac OS X operating system from Apple Computer; a UNIX or Linux type operating system or open source available from many suppliers. Run an operating system that may be what is called; another or future operating system; or some combination thereof. The operating system interfaces with firmware and hardware in a well-known manner to facilitate the integration and execution of various computer program functions that can be written in various programming languages. The operating system typically cooperates with the processor to integrate other components of the computer and perform its functions. The operating system also provides scheduling, input / output control, file and data management, memory management, and communication control and related services, all in accordance with known techniques.

  The system memory can include any of a variety of known or future storage devices that can be used to store desired information and that can be accessed by a computer. Computer-readable storage media include volatile and non-volatile, removable and non-removable media implemented in any method or technique for storing information such as computer readable instructions, data structures, program modules, or other data. sell. Examples include any commonly available random access memory (RAM), read-only memory (ROM), electrically erasable / programmable read-only memory (EEPROM), digital versatile disc (DVD), magnetic media For example, a resident hard disk or tape, an optical medium, such as a read / write compact disk, or other storage device. Storage storage devices may include any of a variety of known or future devices including compact disk drives, tape drives, removable hard disk drives, USB or flash drives, or disk drives. Such types of storage storage devices typically read from and / or write to program storage media such as compact disks, magnetic tapes, removable hard disks, USB or flash drives, or floppy disks. Any of these program storage media, or others currently used or later developed, can be considered computer program products.

  As will be appreciated, these program storage media typically store computer software programs and / or data. Computer software programs, also called computer control logic, are typically stored in a program storage device used in conjunction with system memory and / or storage storage devices. In some embodiments, a computer program product is described that includes a computer usable medium having control logic (a computer software program including program code) stored therein. When executed by the processor, the control logic causes the processor to perform the functions described herein. In other embodiments, some functions are implemented primarily in hardware using, for example, a hardware state machine. It will be apparent to those skilled in the relevant art to implement a hardware state machine to perform the functions described herein. The input / output controller could include any of a variety of known devices for receiving and processing information from a user, whether human, machine, local or remote. Such devices include, for example, modem cards, wireless cards, network interface cards, sound cards, or other types of controllers for any of various known input devices.

  The output controller may include a controller for any of a variety of known display devices for presenting information to the user, whether human, machine, local or remote. In the embodiment described here, the functional elements of the computer communicate with each other via the system bus. In some implementations of the computer, a network or other type of remote communication may be used to interact with several functional elements. As will be apparent to those skilled in the relevant arts, instrument control and / or data processing applications, when implemented in software, can be loaded into and executed from system memory and / or storage devices.

  All or part of the instrument control and / or data processing application may reside in a read-only memory or similar device of the storage device, such device being controlled by the instrument control and / or data processing application It does not need to be loaded first through. It will be appreciated by those skilled in the relevant art that device control and / or data processing applications, or portions thereof, can be loaded by a processor in a known manner into system memory, cache memory, or both, in an advantageous manner for execution Will be understood. The computer can also include one or more library files, experimental data files, and Internet clients stored in system memory. For example, experimental data may include data related to one or more experiments or assays, such as detected signal values, or other values related to one or more sequencing by synthetic (SBS) experiments or processes. I will.

  In addition, an Internet client may include an application that uses a network to access a remote service on another computer, such as what is commonly referred to as a “web browser”. In this example, some commonly used web browsers are Microsoft Internet Explorer available from Microsoft, Mozilla Firefox from Mozilla, Safari from Apple Computer, Google Chrome from Google, or current Google Chrome. Other types of web browsers known or developed in the future are included. Also, in the same or other embodiments, the Internet client may include a specialized software application that allows access to remote information over a network, such as a data processing application for biological applications. Well, or it can be part of the application.

  The network may include one or more of many different types of networks that are well known to those skilled in the art. For example, the network can include a local or wide area network that can use what is commonly referred to as the TCP / IP protocol suite to communicate. The network may include a network that includes a worldwide system of interconnected computer networks, commonly referred to as the Internet, or may also include various intranet architectures. One of ordinary skill in the art will recognize that some users in a network environment may “firewall” (sometimes also referred to as packets, filters, or perimeter protection devices) to control information traffic to hardware and / or software systems. It will also be appreciated that we prefer to use what is commonly referred to as For example, a firewall may include hardware or software components or some combination thereof, and is typically designed to force a user, such as a network administrator, to implement a security policy.

Automated staining system Exemplary automated systems available via Ventana Medical Systems (Tucson, AZ) are the SYMPHONY® staining system (Catalog #: 900-SYM3), VENTANA® BenchMark automated slide preparation. Includes systems (Catalog #: N750-BMKXT-FS, N750-BMKU-FS, VENTANA), and VENTANA® BenchMark special staining automatic slide stainer. These systems employ a microprocessor control system that includes a rotating carousel that supports radially positioned slides. A stepping motor rotates the carousel and places each slide under one of a series of reagent dispensers located above the slide. Bar codes on the slide and reagent dispenser allow computer-controlled positioning of the dispenser and slide so that different reagent treatments can be performed by appropriate computer programming for each of the various tissue samples.

  An exemplary instrumentation system is designed to sequentially apply reagents to tissue pieces mounted on 1/3 inch glass microscope slides under controlled environmental conditions. The instrument includes several basic functions such as reagent application, washing (to remove previously applied reagents), jet draining (technology to reduce residual buffer volume on slide after washing), reagents The application of light oil used to prevent evaporation and other equipment functions shall be implemented. An exemplary staining device processes slides on a rotating carousel. The slide remains stationary and the dispenser carousel rotates the reagent on the stationary slide. The processes described herein can be implemented using a variety of physical configurations. The process of cleaning and staining the tissue on the slide consists of a sequential repetition of the basic instrument functions described above. In essence, the reagent is applied to the tissue and then incubated for a specific time at a specific temperature. When the incubation time is complete, all of the reagents are applied and the reagents are washed and removed from the slide until the staining process is complete, the next reagent is applied, incubated, washed away, etc.

  For related disclosures, see Richards et al., US Pat. No. 6,296,809, assigned to Ventana Medical Systems, where a personalized staining or processing protocol is used where the protocol requires different temperature parameters. However, an apparatus and method for automatically staining or processing multiple tissue samples mounted on a microscope slide so that each sample can be received is disclosed. More specifically, what is described is a computer controlled bar code that automatically applies chemical and biological reagents to tissues or cells mounted or attached to a standard glass microscope slide. It is an apparatus equipped with a dyeing device to be driven. Multiple slides in a circular arrangement on the carousel, commanded by the computer, rotating to a dispensing position and positioning each slide under one of a series of reagent dispensers on a second rotating carousel located above the slide Is mounted. Each slide receives a selected reagent (eg, a DNA probe) and is washed, mixed, and / or heated in an optimal order for the required time.

  The following examples are provided to illustrate certain features of the possible embodiments and general protocols. The scope of the invention is not limited to the features demonstrated by the following examples.

IHC Procedure The following protocol was put into practice with VENTANA® Benchmark XT (VMSI Catalog Number: N750-BMKXT-FS) with NexES V10.6. The range provides exemplary process variation values, and the values in parentheses represent exemplary values:

  (1) Baking can be performed to attach tissue to the slide, especially for new section slides; temperature: 60 ° C-75 ° C; incubation time: 4-32 minutes; for online baking, the paraffin brand used Set to 2-4 degrees higher than the melting point of [no baking];

  (2) Deparaffinization was performed to remove wax for reagent infiltration; unique deparaffinization options include standard, expansion and expansion II; these procedures are for difficult tissue optimization Provides improved flexibility allowing great success; standard is default and uses EZ Prep (VMSI catalog number: 950-102) to reproduce classic deparaffinization protocol; Will reproduce the deparaffinization from HER2 DDISH (VMSI catalog number: 780-4422) (add 5 additional EZ Prep rinsing steps to the standard protocol; extension II, if selected, LCS ( VMSI catalog #: 650-010), [standard deparaffinization, 75 ° C., 4 minutes; EZ Prep rinse; 76 ° C., 4 minutes; rinse will be used;

  (3) Pretreatment; after fixation on slide, optional pretreatment, removal of excess protein (heat and enzyme); fixing reagent that can be filled by user; temperature from 37 ° C to 60 ° C; in reaction buffer 4- Incubation time of “fixing agent 1” to “fixing agent 10” that can be filled by the user for 32 minutes; [no pretreatment];

  (4) Cell conditioning; using CC1 (VMSI catalog number: 950-124), CC2 (VMSI catalog number: 950-123) or reaction buffer (VMSI catalog number: 950-300); slightly for heat + buffer recovery Use CC1 with alkaline pH; use CC2 with pH 6.0 for heat + buffer recovery, use reaction buffer for heat only recovery; 1 to 5 cycles can be selected; incubation time: 4-16 Min: temperature: 60 ° C.-100 ° C. [95 ° C. for 32 minutes—8 minutes followed by 100 ° C. for 24 minutes, CC1];

  (5) Protease treatment: Cell conditioning using heat recovery releases crosslinks from immobilization; Protease “perforates” the protein; Combination can allow better sample penetration; Enzyme selection and incubation time is Reagent manufacturers, recommendations, experiments, enzyme choices; user-fillable dispensers are enzymes 1-10, pre-diluted protease 1-3 (VMSI catalog numbers: 760-2018, 760-2019, 760-2020) Used with pre-diluted ISH-protease 1-3 (VMSI catalog number: 780-4147, 780-4148, 780-4149); incubation time 4-32 minutes [protease free];

  (6) Pre-primary peroxidase inhibition and post-primary peroxidase inhibition (VMSI catalog number: 253-4578); allows the inhibition of endogenous peroxidase after the primary binds to the antigen; This option can improve the staining of the antibody [no peroxidase inhibition is used].

  (7) Rinse reagent; apply reaction buffer; apply LCS, [4 minutes, no heating];

  (8) Clarification process [described here];

  (9) Primary antibody application; choice of primary antibody temperature and primary antibody dilution; allows the user to change the incubation temperature for the primary; add an additional 900 μL of reaction buffer to the slide before applying the primary antibody; Anti-V600E (clone VE1), incubated at 37 ° C. for 16 minutes].

  (10) Detection associates a “visual” molecule with the probe; UltraView or OptiView (VMSI catalog numbers: 760-500, 760-700) [OptiView];

  (11) Counterstain and post-counter stain; add “background” color to tissue; user can select from counter reagent list including pre-diluted VENTANA reagent and user-fillable counterstain dispenser; incubation time from 4 minutes Selectable in up to 32 minutes; [4 minutes counterstaining with hematoxylin II (VMSI catalog number: 790-2208), 4 minutes post-counting staining with bluing reagent (VMSI catalog number: 760-2037)].

Breast Cancer Example Cancerous breast tissue slides were stained with a haptenized primary antibody cocktail followed by sequential staining using an anti-hapten secondary antibody conjugated to HRP. The deposition of specific chromogens of different colors represented the localization of the primary antibody. The stained slide was scanned to obtain the corresponding RGB color image. To analyze the image, we developed a color unmixing algorithm to identify the individual constituent dyes for the biomarker and the proportion of them appearing in the color image. Based on the image channels separated for each marker, a detection algorithm was developed to automatically identify different types of T cells. Image analysis was performed on Oscar, CD3, CD8 and FP3 markers. As the final output, the score for each marker was reported.

  We validated our analysis algorithm by observing different combinations of T cell distribution in the slide and identifying the number and location of each type of T cell.

  Multiplex assays, along with image analysis algorithms, potentially provide a toolkit for the calculation of breast cancer immune scores. Although the final report generated by the computer can be used to support clinical analysis of the patient's personal immune response and help guide further breast cancer treatment, further research is needed to validate this approach. Needed.

  In each case, cell counts and region annotation were performed manually. Each patient's data was summarized as a median formula across the sample nodes, and these values were then compared between relapsed and non-relapsed groups. There was a statistically significant difference in the ambient / internal ratio for both CD3 and CD8, which ratio was higher in non-relapsed patients compared to relapsed patients for both breast cohorts.

Melanoma Example We collected FFPE lymph node dissections from 34 melanoma patients and analyzed a total of 150 lymph nodes. We characterized immune scores by immunohistochemical expression of CD3, CD8, CD20 and Foxp3 (all Ventana Medical Systems). 3-4 μm serial tissue sections were excised against hematoxylin / eosin (H & E) and stained with all multiple markers including the melanoma tumor marker (S100). Tissue sections were stained using the novel multiple staining protocol on the VENTANA benchmark instrument as well as serial section (slide) staining. Initial evaluation was performed using a high power microscopic field (Olympus BX51), excluding bleeding or necrotic areas. The number of positive cells was assessed by counting them around 5 tumors and in 5 intratumoral non-overlapping fields using X400 magnification. The expression of each marker as well as the combination of markers was consistent with the most important clinical information correlating with clinical outcome.

  Although our limited sample size limited some conclusions, the difference in these trends also appeared to be evident for both FoxP3 and CD20. We then assumed a high / low risk score that we are now planning to test for a larger melanoma. In each case, cell counts and region annotation were performed manually. Each patient's data was summarized as a median formula across the sample nodes, and these values were then compared between relapsed and non-relapsed groups. There was a statistically significant difference in the ambient / internal ratio for both CD3 and CD8, which ratio was higher for non-relapsed patients compared to relapsed patients for both proteins. Although our limited sample size limited some conclusions, the difference in these trends was also clearly visible for both FoxP3 and CD20. We then assumed a high / low risk score that we are now planning to validate in a larger cohort.

Image analysis A color unmixing algorithm has been developed that can be referred to as a color unmixing algorithm that separates image data from three or more different chromogens into individual contributions. To scan tissue slides stained with the newly developed multiplex chromogen brightfield assay system with a standard brightfield full slide scanner to provide individual staining-specific image channels for later analysis A color unmixing algorithm was applied. As a model applying this technology, we have developed for breast cancer tissue composed of antibodies targeting CD3 + T cells, CD8 + cytotoxic T cells, FoxP3 + Treg cells, CD20 + B cells, CD68 + macrophages, and cytokeratin + tumor cells. An immune profiling assay was designed.

  We observed a range of immune responses, both in terms of different cell types and in terms of cell number. In addition, immune cells were located in different regions of the tissue, such as only the stroma, the infiltrating margin of the tumor, and the center of the tumor. Various combinations of immune cell distribution have been observed, but these data can predict patient response if correlated with clinical outcome.

Summary The detection and characterization of tumor infiltrating lymphocytes (TILs) and macrophages in individual cancer patients helps to demonstrate to what extent the patient's own immune system is activated in response to the tumor. This information can then be used as a prognosis or prognostic test or as a guide to further treatment.

  Multiple chromogen detection systems are flexible and sensitive techniques that can be used to detect multiple targets with a bright field optical microscope. For example, flexibility is demonstrated by chromogen compatibility, and multiplexed assays can be designed using haptenized antibodies to improve sensitivity and avoid non-specific binding or “crosstalk”. it can. By using hapten amplification, the signal generated is stronger and allows detection of weakly expressed antigens.

  The concept of immunoprofiling illustrated in this application makes it an ideal application for multi-chromogen brightfield IHC, enables multi-component tumor microenvironment analysis, and promotes test efficiency with a single slide staining , Bring medical value.

Claims (16)

A method for scoring an immune response in cancer using tissue infiltrating lymphocytes, comprising:
Contacting the sample with CD3, CD8, CD20, and FoxP3 specific binding components;
Removing excess specific binding components from the sample;
Detecting the specific binding component;
Assessing the presence and distribution of the specific binding component in the sample;
Scoring the cancer using tissue infiltrating lymphocytes by analyzing the presence and distribution of the specific binding component. A method for assisting in scoring an immune response in cancer using tissue infiltrating lymphocytes, comprising:
  Contacting the sample with CD3, CD8, CD20, and FoxP3 specific binding components;
  Removing excess specific binding components from the sample;
  Detecting the specific binding component;
  Assessing the presence and distribution of the specific binding component in the sample;
  Scoring cancer using tissue infiltrating lymphocytes by analyzing the presence and distribution of the specific binding component;
Including a method. 3. The method of claim 1 or 2 , further comprising contacting the sample with a specific binding component for a tumor marker.   4. The method of claim 3, wherein the specific binding component for the tumor marker is selected from an antibody against cytokeratin for epithelial tumor (carcinoma) and an antibody against S100 for melanoma. 5. The method according to any one of claims 1 to 4 , wherein the specific binding component for CD3 is an anti-CD3 rabbit or mouse monoclonal antibody. 6. The method according to any one of claims 1 to 5 , wherein the specific binding component for CD8 is an anti-CD8 rabbit or mouse monoclonal antibody. 7. The method according to any one of claims 1 to 6 , wherein the specific binding component for CD20 is an anti-CD20 rabbit or mouse monoclonal antibody. 8. The method according to any one of claims 1 to 7 , wherein the specific binding component for FoxP3 is an anti-FoxP3 rabbit or mouse monoclonal antibody. Contacting the sample with a CD3, CD8, CD20, and FoxP3 specific binding component contacting the sample with a CD3, CD8, CD20, and FoxP3 specific binding component selected from a rabbit or mouse monoclonal antibody. 5. A method according to any one of claims 1 to 4 comprising. Step of detecting is the sample, comprising contacting a specific anti-species antibody (anti-species antibody) in the rabbit or mouse monoclonal antibodies The method of claim 9. The method of claim 10, anti-species antibody is haptenized or the specific binding, is haptenized, the step of detecting the hapten of the antibody or the specific binding component, said sample, 10. The method according to any one of claims 1 to 9 , further comprising the step of contacting with an anti-hapten antibody conjugated to an enzyme. Further comprising the method of claim 11 the step of step of detecting is contacted with the detection compound the sample. 13. A method according to claim 12 , wherein the detection compound is selected from a chromogen or a fluorophore. Contained in multiple containers,
A CD3 specific binding component and a CD3 detection reagent,
A CD8 specific binding component and a CD8 detection reagent,
A CD20-specific binding component and a CD20 detection reagent,
A FoxP3 specific binding component and a FoxP3 detection reagent;
Detecting the CD3, CD8, CD20, and FoxP3 in tissue samples, including the instructions scoring that described the scoring based on the presence and distribution of the detection reagent on the tissue claim A kit for the method according to 1 . In a system for scoring cancer by examining a tissue sample placed on a glass slide,
A reagent comprising a CD3 specific binding component, a CD3 detection reagent, a CD8 specific binding component, a CD8 detection reagent, a CD20 specific binding component, a CD20 detection reagent, a FoxP3 specific binding component, and a FoxP3 detection reagent,
Including automatic dyeing equipment and imaging equipment,
The autostainer is configured to deliver the reagent onto the slide in such a way that the presence and distribution of CD3, CD8, CD20, and FoxP3 are specifically determined, and the imaging device comprises CD3, A system that is configured such that the presence and distribution of CD8, CD20, and FoxP3 can be used to create a score. A computer-implemented method for counting the number of cell types in a single tissue specimen that is stained in a multiplex assay comprising:
Imaging said single tissue specimen stained in a multiplex assay comprising lymphocyte markers CD3, CD8, CD20, FoxP3 and a tumor detection marker;
Unmixing images of a single tissue specimen stained in a multiplex assay into separate image channels for each marker of the multiplex assay;
Identifying a region of interest in each image channel based on intensity information in each channel, wherein low intensity regions in each channel are removed, and high intensity regions represent cell signals;
Creating a single surrogate image, wherein the surrogate image is a combination of image channel information of all lymphocyte markers;
Applying a cell detection algorithm, wherein the cell detection algorithm is a membrane discovery algorithm or a nuclear discovery algorithm;
Identifying the characteristics of lymphocytes and lymphocyte combinations in each image channel or combined channel image, or a converted image such as a grayscale or absorbance image, or a surrogate image;
Training a classification algorithm based on characteristics of known lymphocytes and lymphocyte combinations;
False positive cells detected cells, CD3 only T cells, CD3 and CD8 T cells, F ox P3 T at least one cell, and CD20B cells as identified to classify detection cells, each image channel or binding channel of the image Applying a trained algorithm to the characteristics of a lymphocyte and a combination of lymphocytes in a transformed image, such as a grayscale or absorbance image, or a surrogate image,
Counting the number of different types of each of the sorted cells;
Scoring the tissue sample, based on the number of each cell type for which the score has been counted ,
Including methods.